Investigation of the structures and chemical ordering of small Pd–Au clusters as a function of composition and potential parameterisation

Abstract

The energetics, structures and segregation of Pd-Au nanoalloys (all compositions for 34- and 38-atoms) have been studied using a genetic algorithm global optimization technique with the Gupta empirical potential. Three modifications of the Pd-Au parameters have been studied: parameter set I in which all parameters (A, xi, p, q and r(0)) in the Gupta potential are weighted in a symmetrical fashion; parameter set II (symmetric weighting of only the pair and many-body energy scaling parameters A and xi); and parameter set III (antisymmetric weighting of A and xi). Structural analysis reveals competition between a range of structural families; decahedra, polyicosahedra and truncated octahedra (for 34 atoms) and incomplete-icosahedra-Mackay, decahedra, polyicosahedra (low-symmetry), six-fold-polyicosahedra and a mixed octahedron-icosahedron (Oh-Ih) structure (for 38 atoms). It is shown that, by finely tuning the Gupta potential, it is possible to qualitatively reproduce the results observed at higher levels of theory (e.g. Density Functional Theory). There are four main types of chemical ordering which are observed: core-shell; spherical cap; ball-and-cup; and mixed. It is shown that the chemical ordering and the proportion of Pd-Au heteronuclear bonds in these clusters are strongly dependent on the potential parameters. Comparison of the results from parameter set III and two previously fitted potentials shows that the DFT-fit potential gives rise to similar results for energies, and lowest energy structures and homotops to those for parameter set III with w(a) = 0.8, but the exp-fit potential gives rise to qualitatively different results.

Cluster energy optimizing genetic algorithm

“…The BCGA has been used to optimize clusters of different types and sizes, and has been combined with different energy optimization methods over the years ( [17], [24], [13], [14], [9], [4], [30], [6]). When it comes to bimetallic clusters, the lack of atom relocating mutations in the BCGA causes it to rely heavily on the L-BFGS local minimization procedure [20] to find the lowest energy clusters.…”

Study of coulomb explosion and dissociation channels in dicationic argon clusters: a study based on stochastic optimization

“…Systems ranging from noble gas atomic clusters (modelled by parametrized Lennard Jones Potential), molecular clusters like water clusters (modelled by TIP2P, TIP3P, SPCE/POL, etc. ), and even metallic and alloy systems (modelled by Gupta Potential) have been very well studied by various researchers [1][2][3][4][5][6][7][8]. These systems pose a formidable challenge from the point of view of optimization, since the number of minima supported by these potentials rise at an astronomical rate with the increase in size of the clusters.…”

Structure, electronic properties and vibrational spectra of (MgF2)nclusters through a combination of genetic algorithm and DFT-based approach

“…The way natural evolution works, with selection of fitter individuals followed by occasional crossover of genetic material between relatively fitter individuals and a provision for mutation have over time led to the creation of better individuals. These concepts if incorporated into an algorithm which goes by the name of genetic algorithm (GA) [26][27][28][29][30][31][32][33][34][35][36][37][38][39][40] can be an efficient problem solver. There are also a group of time-tested techniques which perform a global search on a PES like Simulated Annealing [41][42][43][44][45][47][48][49] and Basin Hopping [50][51][52][53][54][55][56].…”